Overview

Dataset statistics

Number of variables 34
Number of observations 148670
Missing cells 181135
Missing cells (%) 3.6%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 38.6 MiB
Average record size in memory 272.0 B

Variable types

Numeric 11
Categorical 23

Alerts

year has constant value "2019" Constant
Gender is highly overall correlated with co-applicant_credit_type High correlation
Interest_rate_spread is highly overall correlated with Secured_by and 4 other fields High correlation
Secured_by is highly overall correlated with Interest_rate_spread and 4 other fields High correlation
Security_Type is highly overall correlated with Interest_rate_spread and 4 other fields High correlation
Status is highly overall correlated with Interest_rate_spread and 1 other fields High correlation
Upfront_charges is highly overall correlated with Secured_by and 3 other fields High correlation
business_or_commercial is highly overall correlated with loan_type High correlation
co-applicant_credit_type is highly overall correlated with Gender High correlation
construction_type is highly overall correlated with Interest_rate_spread and 4 other fields High correlation
credit_type is highly overall correlated with Status High correlation
income is highly overall correlated with loan_amount and 1 other fields High correlation
loan_amount is highly overall correlated with income and 1 other fields High correlation
loan_type is highly overall correlated with business_or_commercial High correlation
open_credit is highly overall correlated with Upfront_charges High correlation
property_value is highly overall correlated with income and 1 other fields High correlation
rate_of_interest is highly overall correlated with Interest_rate_spread and 3 other fields High correlation
loan_limit is highly imbalanced (63.9%) Imbalance
Credit_Worthiness is highly imbalanced (74.6%) Imbalance
open_credit is highly imbalanced (96.4%) Imbalance
Neg_ammortization is highly imbalanced (52.5%) Imbalance
interest_only is highly imbalanced (72.3%) Imbalance
lump_sum_payment is highly imbalanced (84.3%) Imbalance
construction_type is highly imbalanced (99.7%) Imbalance
occupancy_type is highly imbalanced (72.9%) Imbalance
Secured_by is highly imbalanced (99.7%) Imbalance
total_units is highly imbalanced (93.6%) Imbalance
Security_Type is highly imbalanced (99.7%) Imbalance
loan_limit has 3344 (2.2%) missing values Missing
rate_of_interest has 36439 (24.5%) missing values Missing
Interest_rate_spread has 36639 (24.6%) missing values Missing
Upfront_charges has 39642 (26.7%) missing values Missing
property_value has 15098 (10.2%) missing values Missing
income has 9150 (6.2%) missing values Missing
LTV has 15098 (10.2%) missing values Missing
dtir1 has 24121 (16.2%) missing values Missing
LTV is highly skewed (γ1 = 120.6153375) Skewed
ID is uniformly distributed Uniform
ID has unique values Unique
Upfront_charges has 20770 (14.0%) zeros Zeros

Reproduction

Analysis started 2025-11-26 17:28:27.614795
Analysis finished 2025-11-26 17:28:59.614508
Duration 32 seconds
Software version ydata-profiling vv4.18.0
Download configuration config.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct 148670
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 99224.5
Minimum 24890
Maximum 173559
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:58:59.688190 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 24890
5-th percentile 32323.45
Q1 62057.25
median 99224.5
Q3 136391.75
95-th percentile 166125.55
Maximum 173559
Range 148669
Interquartile range (IQR) 74334.5

Descriptive statistics

Standard deviation 42917.477
Coefficient of variation (CV) 0.43252903
Kurtosis -1.2
Mean 99224.5
Median Absolute Deviation (MAD) 37167.5
Skewness 0
Sum 1.4751706 × 1010
Variance 1.8419098 × 109
Monotonicity Strictly increasing
2025-11-26T22:58:59.795516 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
24890 1
 
< 0.1%
24891 1
 
< 0.1%
24892 1
 
< 0.1%
24893 1
 
< 0.1%
24894 1
 
< 0.1%
24895 1
 
< 0.1%
24896 1
 
< 0.1%
24897 1
 
< 0.1%
24898 1
 
< 0.1%
24899 1
 
< 0.1%
Other values (148660) 148660
> 99.9%
Value Count Frequency (%)
24890 1
< 0.1%
24891 1
< 0.1%
24892 1
< 0.1%
24893 1
< 0.1%
24894 1
< 0.1%
24895 1
< 0.1%
24896 1
< 0.1%
24897 1
< 0.1%
24898 1
< 0.1%
24899 1
< 0.1%
Value Count Frequency (%)
173559 1
< 0.1%
173558 1
< 0.1%
173557 1
< 0.1%
173556 1
< 0.1%
173555 1
< 0.1%
173554 1
< 0.1%
173553 1
< 0.1%
173552 1
< 0.1%
173551 1
< 0.1%
173550 1
< 0.1%

year
Categorical

Constant 

Distinct 1
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
2019
148670 

Length

Max length 4
Median length 4
Mean length 4
Min length 4

Characters and Unicode

Total characters 594680
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 2019
2nd row 2019
3rd row 2019
4th row 2019
5th row 2019

Common Values

Value Count Frequency (%)
2019 148670
100.0%

Length

2025-11-26T22:58:59.897627 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:58:59.957017 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
2019 148670
100.0%

Most occurring characters

Value Count Frequency (%)
2 148670
25.0%
0 148670
25.0%
1 148670
25.0%
9 148670
25.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 594680
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
2 148670
25.0%
0 148670
25.0%
1 148670
25.0%
9 148670
25.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 594680
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
2 148670
25.0%
0 148670
25.0%
1 148670
25.0%
9 148670
25.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 594680
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
2 148670
25.0%
0 148670
25.0%
1 148670
25.0%
9 148670
25.0%

loan_limit
Categorical

Imbalance  Missing 

Distinct 2
Distinct (%) < 0.1%
Missing 3344
Missing (%) 2.2%
Memory size 1.1 MiB
cf
135348 
ncf
 
9978

Length

Max length 3
Median length 2
Mean length 2.0686594
Min length 2

Characters and Unicode

Total characters 300630
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row cf
2nd row cf
3rd row cf
4th row cf
5th row cf

Common Values

Value Count Frequency (%)
cf 135348
91.0%
ncf 9978
 
6.7%
(Missing) 3344
 
2.2%

Length

2025-11-26T22:59:00.014927 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:00.064266 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
cf 135348
93.1%
ncf 9978
 
6.9%

Most occurring characters

Value Count Frequency (%)
c 145326
48.3%
f 145326
48.3%
n 9978
 
3.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 300630
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
c 145326
48.3%
f 145326
48.3%
n 9978
 
3.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 300630
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
c 145326
48.3%
f 145326
48.3%
n 9978
 
3.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 300630
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
c 145326
48.3%
f 145326
48.3%
n 9978
 
3.3%

Gender
Categorical

High correlation 

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
Male
42346 
Joint
41399 
Sex Not Available
37659 
Female
27266 

Length

Max length 17
Median length 6
Mean length 7.9382391
Min length 4

Characters and Unicode

Total characters 1180178
Distinct characters 18
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Sex Not Available
2nd row Male
3rd row Male
4th row Male
5th row Joint

Common Values

Value Count Frequency (%)
Male 42346
28.5%
Joint 41399
27.8%
Sex Not Available 37659
25.3%
Female 27266
18.3%

Length

2025-11-26T22:59:00.123203 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:00.182369 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
male 42346
18.9%
joint 41399
18.5%
sex 37659
16.8%
not 37659
16.8%
available 37659
16.8%
female 27266
12.2%

Most occurring characters

Value Count Frequency (%)
e 172196
14.6%
a 144930
12.3%
l 144930
12.3%
t 79058
 
6.7%
o 79058
 
6.7%
i 79058
 
6.7%
75318
 
6.4%
M 42346
 
3.6%
n 41399
 
3.5%
J 41399
 
3.5%
Other values (8) 280486
23.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 1180178
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 172196
14.6%
a 144930
12.3%
l 144930
12.3%
t 79058
 
6.7%
o 79058
 
6.7%
i 79058
 
6.7%
75318
 
6.4%
M 42346
 
3.6%
n 41399
 
3.5%
J 41399
 
3.5%
Other values (8) 280486
23.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1180178
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 172196
14.6%
a 144930
12.3%
l 144930
12.3%
t 79058
 
6.7%
o 79058
 
6.7%
i 79058
 
6.7%
75318
 
6.4%
M 42346
 
3.6%
n 41399
 
3.5%
J 41399
 
3.5%
Other values (8) 280486
23.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1180178
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 172196
14.6%
a 144930
12.3%
l 144930
12.3%
t 79058
 
6.7%
o 79058
 
6.7%
i 79058
 
6.7%
75318
 
6.4%
M 42346
 
3.6%
n 41399
 
3.5%
J 41399
 
3.5%
Other values (8) 280486
23.8%

approv_in_adv
Categorical

Distinct 2
Distinct (%) < 0.1%
Missing 908
Missing (%) 0.6%
Memory size 1.1 MiB
nopre
124621 
pre
23141 

Length

Max length 5
Median length 5
Mean length 4.6867801
Min length 3

Characters and Unicode

Total characters 692528
Distinct characters 5
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row nopre
2nd row nopre
3rd row pre
4th row nopre
5th row pre

Common Values

Value Count Frequency (%)
nopre 124621
83.8%
pre 23141
 
15.6%
(Missing) 908
 
0.6%

Length

2025-11-26T22:59:00.269453 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:00.324697 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
nopre 124621
84.3%
pre 23141
 
15.7%

Most occurring characters

Value Count Frequency (%)
p 147762
21.3%
e 147762
21.3%
r 147762
21.3%
n 124621
18.0%
o 124621
18.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 692528
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
p 147762
21.3%
e 147762
21.3%
r 147762
21.3%
n 124621
18.0%
o 124621
18.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 692528
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
p 147762
21.3%
e 147762
21.3%
r 147762
21.3%
n 124621
18.0%
o 124621
18.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 692528
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
p 147762
21.3%
e 147762
21.3%
r 147762
21.3%
n 124621
18.0%
o 124621
18.0%

loan_type
Categorical

High correlation 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
type1
113173 
type2
20762 
type3
14735 

Length

Max length 5
Median length 5
Mean length 5
Min length 5

Characters and Unicode

Total characters 743350
Distinct characters 7
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row type1
2nd row type2
3rd row type1
4th row type1
5th row type1

Common Values

Value Count Frequency (%)
type1 113173
76.1%
type2 20762
 
14.0%
type3 14735
 
9.9%

Length

2025-11-26T22:59:00.388265 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:00.450277 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
type1 113173
76.1%
type2 20762
 
14.0%
type3 14735
 
9.9%

Most occurring characters

Value Count Frequency (%)
t 148670
20.0%
y 148670
20.0%
p 148670
20.0%
e 148670
20.0%
1 113173
15.2%
2 20762
 
2.8%
3 14735
 
2.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 743350
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
t 148670
20.0%
y 148670
20.0%
p 148670
20.0%
e 148670
20.0%
1 113173
15.2%
2 20762
 
2.8%
3 14735
 
2.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 743350
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
t 148670
20.0%
y 148670
20.0%
p 148670
20.0%
e 148670
20.0%
1 113173
15.2%
2 20762
 
2.8%
3 14735
 
2.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 743350
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
t 148670
20.0%
y 148670
20.0%
p 148670
20.0%
e 148670
20.0%
1 113173
15.2%
2 20762
 
2.8%
3 14735
 
2.0%

loan_purpose
Categorical

Distinct 4
Distinct (%) < 0.1%
Missing 134
Missing (%) 0.1%
Memory size 1.1 MiB
p3
55934 
p4
54799 
p1
34529 
p2
 
3274

Length

Max length 2
Median length 2
Mean length 2
Min length 2

Characters and Unicode

Total characters 297072
Distinct characters 5
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row p1
2nd row p1
3rd row p1
4th row p4
5th row p1

Common Values

Value Count Frequency (%)
p3 55934
37.6%
p4 54799
36.9%
p1 34529
23.2%
p2 3274
 
2.2%
(Missing) 134
 
0.1%

Length

2025-11-26T22:59:00.527674 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:00.591928 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
p3 55934
37.7%
p4 54799
36.9%
p1 34529
23.2%
p2 3274
 
2.2%

Most occurring characters

Value Count Frequency (%)
p 148536
50.0%
3 55934
 
18.8%
4 54799
 
18.4%
1 34529
 
11.6%
2 3274
 
1.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 297072
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
p 148536
50.0%
3 55934
 
18.8%
4 54799
 
18.4%
1 34529
 
11.6%
2 3274
 
1.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 297072
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
p 148536
50.0%
3 55934
 
18.8%
4 54799
 
18.4%
1 34529
 
11.6%
2 3274
 
1.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 297072
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
p 148536
50.0%
3 55934
 
18.8%
4 54799
 
18.4%
1 34529
 
11.6%
2 3274
 
1.1%

Credit_Worthiness
Categorical

Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
l1
142344 
l2
 
6326

Length

Max length 2
Median length 2
Mean length 2
Min length 2

Characters and Unicode

Total characters 297340
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row l1
2nd row l1
3rd row l1
4th row l1
5th row l1

Common Values

Value Count Frequency (%)
l1 142344
95.7%
l2 6326
 
4.3%

Length

2025-11-26T22:59:00.673422 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:00.730917 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
l1 142344
95.7%
l2 6326
 
4.3%

Most occurring characters

Value Count Frequency (%)
l 148670
50.0%
1 142344
47.9%
2 6326
 
2.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
l 148670
50.0%
1 142344
47.9%
2 6326
 
2.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
l 148670
50.0%
1 142344
47.9%
2 6326
 
2.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
l 148670
50.0%
1 142344
47.9%
2 6326
 
2.1%

open_credit
Categorical

High correlation  Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
nopc
148114 
opc
 
556

Length

Max length 4
Median length 4
Mean length 3.9962602
Min length 3

Characters and Unicode

Total characters 594124
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row nopc
2nd row nopc
3rd row nopc
4th row nopc
5th row nopc

Common Values

Value Count Frequency (%)
nopc 148114
99.6%
opc 556
 
0.4%

Length

2025-11-26T22:59:00.794027 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:00.847168 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
nopc 148114
99.6%
opc 556
 
0.4%

Most occurring characters

Value Count Frequency (%)
o 148670
25.0%
p 148670
25.0%
c 148670
25.0%
n 148114
24.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 594124
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
o 148670
25.0%
p 148670
25.0%
c 148670
25.0%
n 148114
24.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 594124
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
o 148670
25.0%
p 148670
25.0%
c 148670
25.0%
n 148114
24.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 594124
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
o 148670
25.0%
p 148670
25.0%
c 148670
25.0%
n 148114
24.9%

business_or_commercial
Categorical

High correlation 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
nob/c
127908 
b/c
20762 

Length

Max length 5
Median length 5
Mean length 4.7206968
Min length 3

Characters and Unicode

Total characters 701826
Distinct characters 5
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row nob/c
2nd row b/c
3rd row nob/c
4th row nob/c
5th row nob/c

Common Values

Value Count Frequency (%)
nob/c 127908
86.0%
b/c 20762
 
14.0%

Length

2025-11-26T22:59:00.918129 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:00.973418 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
nob/c 127908
86.0%
b/c 20762
 
14.0%

Most occurring characters

Value Count Frequency (%)
b 148670
21.2%
c 148670
21.2%
/148670
21.2%
n 127908
18.2%
o 127908
18.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 701826
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
b 148670
21.2%
c 148670
21.2%
/148670
21.2%
n 127908
18.2%
o 127908
18.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 701826
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
b 148670
21.2%
c 148670
21.2%
/148670
21.2%
n 127908
18.2%
o 127908
18.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 701826
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
b 148670
21.2%
c 148670
21.2%
/148670
21.2%
n 127908
18.2%
o 127908
18.2%

loan_amount
Real number (ℝ)

High correlation 

Distinct 211
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 331117.74
Minimum 16500
Maximum 3576500
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:01.044534 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 16500
5-th percentile 106500
Q1 196500
median 296500
Q3 436500
95-th percentile 656500
Maximum 3576500
Range 3560000
Interquartile range (IQR) 240000

Descriptive statistics

Standard deviation 183909.31
Coefficient of variation (CV) 0.55541968
Kurtosis 9.1277753
Mean 331117.74
Median Absolute Deviation (MAD) 120000
Skewness 1.6669981
Sum 4.9227275 × 1010
Variance 3.3822634 × 1010
Monotonicity Not monotonic
2025-11-26T22:59:01.143451 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
206500 4610
 
3.1%
256500 4079
 
2.7%
156500 3967
 
2.7%
226500 3944
 
2.7%
486500 3819
 
2.6%
306500 3691
 
2.5%
246500 3669
 
2.5%
216500 3649
 
2.5%
236500 3553
 
2.4%
266500 3543
 
2.4%
Other values (201) 110146
74.1%
Value Count Frequency (%)
16500 3
 
< 0.1%
26500 27
 
< 0.1%
36500 119
 
0.1%
46500 212
 
0.1%
56500 810
 
0.5%
66500 859
 
0.6%
76500 1701
1.1%
86500 1605
1.1%
96500 1484
1.0%
106500 3210
2.2%
Value Count Frequency (%)
3576500 1
 
< 0.1%
3346500 1
 
< 0.1%
3006500 4
< 0.1%
2986500 1
 
< 0.1%
2926500 1
 
< 0.1%
2706500 1
 
< 0.1%
2626500 1
 
< 0.1%
2606500 1
 
< 0.1%
2596500 1
 
< 0.1%
2506500 2
< 0.1%

rate_of_interest
Real number (ℝ)

High correlation  Missing 

Distinct 131
Distinct (%) 0.1%
Missing 36439
Missing (%) 24.5%
Infinite 0
Infinite (%) 0.0%
Mean 4.0454758
Minimum 0
Maximum 8
Zeros 1
Zeros (%) < 0.1%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:01.243630 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 3.125
Q1 3.625
median 3.99
Q3 4.375
95-th percentile 4.99
Maximum 8
Range 8
Interquartile range (IQR) 0.75

Descriptive statistics

Standard deviation 0.56139119
Coefficient of variation (CV) 0.13877013
Kurtosis 0.34456404
Mean 4.0454758
Median Absolute Deviation (MAD) 0.365
Skewness 0.38840603
Sum 454027.79
Variance 0.31516007
Monotonicity Not monotonic
2025-11-26T22:59:01.348372 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
3.99 14455
 
9.7%
3.625 8800
 
5.9%
3.875 8592
 
5.8%
3.75 8474
 
5.7%
3.5 6866
 
4.6%
4.5 6809
 
4.6%
4.375 6482
 
4.4%
4.25 6045
 
4.1%
4.125 5797
 
3.9%
4.75 4875
 
3.3%
Other values (121) 35036
23.6%
(Missing) 36439
24.5%
Value Count Frequency (%)
0 1
 
< 0.1%
2.125 1
 
< 0.1%
2.25 4
 
< 0.1%
2.375 2
 
< 0.1%
2.475 2
 
< 0.1%
2.5 21
< 0.1%
2.575 1
 
< 0.1%
2.6 3
 
< 0.1%
2.625 25
< 0.1%
2.65 2
 
< 0.1%
Value Count Frequency (%)
8 1
 
< 0.1%
7.75 1
 
< 0.1%
7.5 2
 
< 0.1%
7.375 1
 
< 0.1%
7.125 1
 
< 0.1%
7 1
 
< 0.1%
6.875 1
 
< 0.1%
6.75 5
< 0.1%
6.5 3
< 0.1%
6.375 1
 
< 0.1%

Interest_rate_spread
Real number (ℝ)

High correlation  Missing 

Distinct 22516
Distinct (%) 20.1%
Missing 36639
Missing (%) 24.6%
Infinite 0
Infinite (%) 0.0%
Mean 0.44165566
Minimum -3.638
Maximum 3.357
Zeros 9
Zeros (%) < 0.1%
Negative 21883
Negative (%) 14.7%
Memory size 1.1 MiB
2025-11-26T22:59:01.441638 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum -3.638
5-th percentile -0.31745
Q1 0.076
median 0.3904
Q3 0.7754
95-th percentile 1.3794
Maximum 3.357
Range 6.995
Interquartile range (IQR) 0.6994

Descriptive statistics

Standard deviation 0.51304274
Coefficient of variation (CV) 1.1616351
Kurtosis -0.18356608
Mean 0.44165566
Median Absolute Deviation (MAD) 0.3427
Skewness 0.28076233
Sum 49479.125
Variance 0.26321285
Monotonicity Not monotonic
2025-11-26T22:59:01.537517 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-0.028 77
 
0.1%
-0.038 64
 
< 0.1%
-0.023 60
 
< 0.1%
-0.173 56
 
< 0.1%
-0.148 52
 
< 0.1%
0.202 51
 
< 0.1%
0.252 51
 
< 0.1%
0.112 46
 
< 0.1%
0.257 46
 
< 0.1%
-0.013 45
 
< 0.1%
Other values (22506) 111483
75.0%
(Missing) 36639
 
24.6%
Value Count Frequency (%)
-3.638 1
< 0.1%
-1.0841 1
< 0.1%
-1.047 1
< 0.1%
-1.0462 1
< 0.1%
-1.039 1
< 0.1%
-1.038 1
< 0.1%
-1.0379 1
< 0.1%
-1.0343 1
< 0.1%
-1.0294 1
< 0.1%
-1.0288 1
< 0.1%
Value Count Frequency (%)
3.357 1
< 0.1%
2.8854 1
< 0.1%
2.7227 1
< 0.1%
2.6368 1
< 0.1%
2.5932 1
< 0.1%
2.5851 1
< 0.1%
2.537 1
< 0.1%
2.5144 1
< 0.1%
2.4093 1
< 0.1%
2.182 1
< 0.1%

Upfront_charges
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct 58271
Distinct (%) 53.4%
Missing 39642
Missing (%) 26.7%
Infinite 0
Infinite (%) 0.0%
Mean 3224.9961
Minimum 0
Maximum 60000
Zeros 20770
Zeros (%) 14.0%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:01.631841 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 581.49
median 2596.45
Q3 4812.5
95-th percentile 9272.6885
Maximum 60000
Range 60000
Interquartile range (IQR) 4231.01

Descriptive statistics

Standard deviation 3251.1215
Coefficient of variation (CV) 1.0081009
Kurtosis 6.3685863
Mean 3224.9961
Median Absolute Deviation (MAD) 2108.66
Skewness 1.7540757
Sum 3.5161488 × 108
Variance 10569791
Monotonicity Not monotonic
2025-11-26T22:59:01.734843 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 20770
 
14.0%
1250 1184
 
0.8%
1150 892
 
0.6%
795 487
 
0.3%
295 403
 
0.3%
950 192
 
0.1%
3000 173
 
0.1%
995 151
 
0.1%
4000 149
 
0.1%
5000 147
 
0.1%
Other values (58261) 84480
56.8%
(Missing) 39642
26.7%
Value Count Frequency (%)
0 20770
14.0%
0.03 1
 
< 0.1%
0.06 1
 
< 0.1%
0.35 1
 
< 0.1%
0.6 1
 
< 0.1%
0.72 1
 
< 0.1%
0.75 1
 
< 0.1%
0.92 1
 
< 0.1%
1 12
 
< 0.1%
1.15 1
 
< 0.1%
Value Count Frequency (%)
60000 1
< 0.1%
53485.78 1
< 0.1%
38437.5 1
< 0.1%
38375 1
< 0.1%
37604.38 1
< 0.1%
35192.5 1
< 0.1%
33268 1
< 0.1%
32850 1
< 0.1%
32825.25 1
< 0.1%
32647 1
< 0.1%

term
Real number (ℝ)

Distinct 26
Distinct (%) < 0.1%
Missing 41
Missing (%) < 0.1%
Infinite 0
Infinite (%) 0.0%
Mean 335.13658
Minimum 96
Maximum 360
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:01.953660 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 96
5-th percentile 180
Q1 360
median 360
Q3 360
95-th percentile 360
Maximum 360
Range 264
Interquartile range (IQR) 0

Descriptive statistics

Standard deviation 58.409084
Coefficient of variation (CV) 0.17428442
Kurtosis 3.1732363
Mean 335.13658
Median Absolute Deviation (MAD) 0
Skewness -2.1748218
Sum 49811015
Variance 3411.621
Monotonicity Not monotonic
2025-11-26T22:59:02.031509 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
Value Count Frequency (%)
360 121685
81.8%
180 12981
 
8.7%
240 5859
 
3.9%
300 2822
 
1.9%
324 2766
 
1.9%
120 510
 
0.3%
144 263
 
0.2%
348 260
 
0.2%
336 213
 
0.1%
96 194
 
0.1%
Other values (16) 1076
 
0.7%
Value Count Frequency (%)
96 194
 
0.1%
108 33
 
< 0.1%
120 510
 
0.3%
132 93
 
0.1%
144 263
 
0.2%
156 174
 
0.1%
165 1
 
< 0.1%
168 82
 
0.1%
180 12981
8.7%
192 17
 
< 0.1%
Value Count Frequency (%)
360 121685
81.8%
348 260
 
0.2%
336 213
 
0.1%
324 2766
 
1.9%
322 1
 
< 0.1%
312 185
 
0.1%
300 2822
 
1.9%
288 90
 
0.1%
280 1
 
< 0.1%
276 100
 
0.1%

Neg_ammortization
Categorical

Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 121
Missing (%) 0.1%
Memory size 1.1 MiB
not_neg
133420 
neg_amm
15129 

Length

Max length 7
Median length 7
Mean length 7
Min length 7

Characters and Unicode

Total characters 1039843
Distinct characters 8
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row not_neg
2nd row not_neg
3rd row neg_amm
4th row not_neg
5th row not_neg

Common Values

Value Count Frequency (%)
not_neg 133420
89.7%
neg_amm 15129
 
10.2%
(Missing) 121
 
0.1%

Length

2025-11-26T22:59:02.114826 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:02.164731 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
not_neg 133420
89.8%
neg_amm 15129
 
10.2%

Most occurring characters

Value Count Frequency (%)
n 281969
27.1%
_ 148549
14.3%
g 148549
14.3%
e 148549
14.3%
t 133420
12.8%
o 133420
12.8%
m 30258
 
2.9%
a 15129
 
1.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 1039843
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
n 281969
27.1%
_ 148549
14.3%
g 148549
14.3%
e 148549
14.3%
t 133420
12.8%
o 133420
12.8%
m 30258
 
2.9%
a 15129
 
1.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1039843
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
n 281969
27.1%
_ 148549
14.3%
g 148549
14.3%
e 148549
14.3%
t 133420
12.8%
o 133420
12.8%
m 30258
 
2.9%
a 15129
 
1.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1039843
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
n 281969
27.1%
_ 148549
14.3%
g 148549
14.3%
e 148549
14.3%
t 133420
12.8%
o 133420
12.8%
m 30258
 
2.9%
a 15129
 
1.5%

interest_only
Categorical

Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
not_int
141560 
int_only
 
7110

Length

Max length 8
Median length 7
Mean length 7.047824
Min length 7

Characters and Unicode

Total characters 1047800
Distinct characters 7
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row not_int
2nd row not_int
3rd row not_int
4th row not_int
5th row not_int

Common Values

Value Count Frequency (%)
not_int 141560
95.2%
int_only 7110
 
4.8%

Length

2025-11-26T22:59:02.230031 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:02.278350 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
not_int 141560
95.2%
int_only 7110
 
4.8%

Most occurring characters

Value Count Frequency (%)
n 297340
28.4%
t 290230
27.7%
o 148670
14.2%
_ 148670
14.2%
i 148670
14.2%
l 7110
 
0.7%
y 7110
 
0.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 1047800
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
n 297340
28.4%
t 290230
27.7%
o 148670
14.2%
_ 148670
14.2%
i 148670
14.2%
l 7110
 
0.7%
y 7110
 
0.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1047800
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
n 297340
28.4%
t 290230
27.7%
o 148670
14.2%
_ 148670
14.2%
i 148670
14.2%
l 7110
 
0.7%
y 7110
 
0.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1047800
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
n 297340
28.4%
t 290230
27.7%
o 148670
14.2%
_ 148670
14.2%
i 148670
14.2%
l 7110
 
0.7%
y 7110
 
0.7%

lump_sum_payment
Categorical

Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
not_lpsm
145286 
lpsm
 
3384

Length

Max length 8
Median length 8
Mean length 7.9089527
Min length 4

Characters and Unicode

Total characters 1175824
Distinct characters 8
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row not_lpsm
2nd row lpsm
3rd row not_lpsm
4th row not_lpsm
5th row not_lpsm

Common Values

Value Count Frequency (%)
not_lpsm 145286
97.7%
lpsm 3384
 
2.3%

Length

2025-11-26T22:59:02.346018 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:02.398075 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
not_lpsm 145286
97.7%
lpsm 3384
 
2.3%

Most occurring characters

Value Count Frequency (%)
m 148670
12.6%
s 148670
12.6%
p 148670
12.6%
l 148670
12.6%
_ 145286
12.4%
t 145286
12.4%
o 145286
12.4%
n 145286
12.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 1175824
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
m 148670
12.6%
s 148670
12.6%
p 148670
12.6%
l 148670
12.6%
_ 145286
12.4%
t 145286
12.4%
o 145286
12.4%
n 145286
12.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1175824
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
m 148670
12.6%
s 148670
12.6%
p 148670
12.6%
l 148670
12.6%
_ 145286
12.4%
t 145286
12.4%
o 145286
12.4%
n 145286
12.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1175824
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
m 148670
12.6%
s 148670
12.6%
p 148670
12.6%
l 148670
12.6%
_ 145286
12.4%
t 145286
12.4%
o 145286
12.4%
n 145286
12.4%

property_value
Real number (ℝ)

High correlation  Missing 

Distinct 385
Distinct (%) 0.3%
Missing 15098
Missing (%) 10.2%
Infinite 0
Infinite (%) 0.0%
Mean 497893.47
Minimum 8000
Maximum 16508000
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:02.462848 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 8000
5-th percentile 148000
Q1 268000
median 418000
Q3 628000
95-th percentile 1058000
Maximum 16508000
Range 16500000
Interquartile range (IQR) 360000

Descriptive statistics

Standard deviation 359935.32
Coefficient of variation (CV) 0.72291633
Kurtosis 73.221196
Mean 497893.47
Median Absolute Deviation (MAD) 170000
Skewness 4.5862758
Sum 6.6504626 × 1010
Variance 1.2955343 × 1011
Monotonicity Not monotonic
2025-11-26T22:59:02.558631 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
308000 2792
 
1.9%
258000 2763
 
1.9%
358000 2679
 
1.8%
408000 2537
 
1.7%
328000 2524
 
1.7%
278000 2513
 
1.7%
268000 2497
 
1.7%
228000 2493
 
1.7%
238000 2408
 
1.6%
288000 2398
 
1.6%
Other values (375) 107968
72.6%
(Missing) 15098
 
10.2%
Value Count Frequency (%)
8000 6
 
< 0.1%
18000 1
 
< 0.1%
28000 9
 
< 0.1%
38000 35
 
< 0.1%
48000 71
 
< 0.1%
58000 141
 
0.1%
68000 271
0.2%
78000 387
0.3%
88000 568
0.4%
98000 556
0.4%
Value Count Frequency (%)
16508000 1
< 0.1%
12008000 1
< 0.1%
11008000 1
< 0.1%
10008000 1
< 0.1%
9268000 1
< 0.1%
8508000 1
< 0.1%
7608000 1
< 0.1%
6908000 1
< 0.1%
6508000 1
< 0.1%
6408000 1
< 0.1%

construction_type
Categorical

High correlation  Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
sb
148637 
mh
 
33

Length

Max length 2
Median length 2
Mean length 2
Min length 2

Characters and Unicode

Total characters 297340
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row sb
2nd row sb
3rd row sb
4th row sb
5th row sb

Common Values

Value Count Frequency (%)
sb 148637
> 99.9%
mh 33
 
< 0.1%

Length

2025-11-26T22:59:02.653323 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:02.708913 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
sb 148637
> 99.9%
mh 33
 
< 0.1%

Most occurring characters

Value Count Frequency (%)
s 148637
50.0%
b 148637
50.0%
m 33
 
< 0.1%
h 33
 
< 0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
s 148637
50.0%
b 148637
50.0%
m 33
 
< 0.1%
h 33
 
< 0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
s 148637
50.0%
b 148637
50.0%
m 33
 
< 0.1%
h 33
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
s 148637
50.0%
b 148637
50.0%
m 33
 
< 0.1%
h 33
 
< 0.1%

occupancy_type
Categorical

Imbalance 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
pr
138201 
ir
 
7340
sr
 
3129

Length

Max length 2
Median length 2
Mean length 2
Min length 2

Characters and Unicode

Total characters 297340
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row pr
2nd row pr
3rd row pr
4th row pr
5th row pr

Common Values

Value Count Frequency (%)
pr 138201
93.0%
ir 7340
 
4.9%
sr 3129
 
2.1%

Length

2025-11-26T22:59:02.777124 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:02.832963 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
pr 138201
93.0%
ir 7340
 
4.9%
sr 3129
 
2.1%

Most occurring characters

Value Count Frequency (%)
r 148670
50.0%
p 138201
46.5%
i 7340
 
2.5%
s 3129
 
1.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
r 148670
50.0%
p 138201
46.5%
i 7340
 
2.5%
s 3129
 
1.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
r 148670
50.0%
p 138201
46.5%
i 7340
 
2.5%
s 3129
 
1.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
r 148670
50.0%
p 138201
46.5%
i 7340
 
2.5%
s 3129
 
1.1%

Secured_by
Categorical

High correlation  Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
home
148637 
land
 
33

Length

Max length 4
Median length 4
Mean length 4
Min length 4

Characters and Unicode

Total characters 594680
Distinct characters 8
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row home
2nd row home
3rd row home
4th row home
5th row home

Common Values

Value Count Frequency (%)
home 148637
> 99.9%
land 33
 
< 0.1%

Length

2025-11-26T22:59:02.898662 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:02.950548 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
home 148637
> 99.9%
land 33
 
< 0.1%

Most occurring characters

Value Count Frequency (%)
h 148637
25.0%
o 148637
25.0%
m 148637
25.0%
e 148637
25.0%
l 33
 
< 0.1%
a 33
 
< 0.1%
n 33
 
< 0.1%
d 33
 
< 0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 594680
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
h 148637
25.0%
o 148637
25.0%
m 148637
25.0%
e 148637
25.0%
l 33
 
< 0.1%
a 33
 
< 0.1%
n 33
 
< 0.1%
d 33
 
< 0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 594680
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
h 148637
25.0%
o 148637
25.0%
m 148637
25.0%
e 148637
25.0%
l 33
 
< 0.1%
a 33
 
< 0.1%
n 33
 
< 0.1%
d 33
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 594680
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
h 148637
25.0%
o 148637
25.0%
m 148637
25.0%
e 148637
25.0%
l 33
 
< 0.1%
a 33
 
< 0.1%
n 33
 
< 0.1%
d 33
 
< 0.1%

total_units
Categorical

Imbalance 

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
1U
146480 
2U
 
1477
3U
 
393
4U
 
320

Length

Max length 2
Median length 2
Mean length 2
Min length 2

Characters and Unicode

Total characters 297340
Distinct characters 5
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1U
2nd row 1U
3rd row 1U
4th row 1U
5th row 1U

Common Values

Value Count Frequency (%)
1U 146480
98.5%
2U 1477
 
1.0%
3U 393
 
0.3%
4U 320
 
0.2%

Length

2025-11-26T22:59:03.013547 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:03.071925 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
1u 146480
98.5%
2u 1477
 
1.0%
3u 393
 
0.3%
4u 320
 
0.2%

Most occurring characters

Value Count Frequency (%)
U 148670
50.0%
1 146480
49.3%
2 1477
 
0.5%
3 393
 
0.1%
4 320
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
U 148670
50.0%
1 146480
49.3%
2 1477
 
0.5%
3 393
 
0.1%
4 320
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
U 148670
50.0%
1 146480
49.3%
2 1477
 
0.5%
3 393
 
0.1%
4 320
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 297340
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
U 148670
50.0%
1 146480
49.3%
2 1477
 
0.5%
3 393
 
0.1%
4 320
 
0.1%

income
Real number (ℝ)

High correlation  Missing 

Distinct 1001
Distinct (%) 0.7%
Missing 9150
Missing (%) 6.2%
Infinite 0
Infinite (%) 0.0%
Mean 6957.3389
Minimum 0
Maximum 578580
Zeros 1260
Zeros (%) 0.8%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:03.147471 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 1920
Q1 3720
median 5760
Q3 8520
95-th percentile 15420
Maximum 578580
Range 578580
Interquartile range (IQR) 4800

Descriptive statistics

Standard deviation 6496.5864
Coefficient of variation (CV) 0.93377461
Kurtosis 885.29246
Mean 6957.3389
Median Absolute Deviation (MAD) 2280
Skewness 17.307695
Sum 9.7068792 × 108
Variance 42205635
Monotonicity Not monotonic
2025-11-26T22:59:03.247058 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 1260
 
0.8%
3600 1250
 
0.8%
4200 1243
 
0.8%
4800 1191
 
0.8%
3120 1168
 
0.8%
3720 1161
 
0.8%
3900 1159
 
0.8%
5400 1152
 
0.8%
3300 1144
 
0.8%
4500 1139
 
0.8%
Other values (991) 127653
85.9%
(Missing) 9150
 
6.2%
Value Count Frequency (%)
0 1260
0.8%
60 5
 
< 0.1%
120 12
 
< 0.1%
180 12
 
< 0.1%
240 15
 
< 0.1%
300 18
 
< 0.1%
360 11
 
< 0.1%
420 15
 
< 0.1%
480 11
 
< 0.1%
540 17
 
< 0.1%
Value Count Frequency (%)
578580 1
< 0.1%
377220 1
< 0.1%
374400 1
< 0.1%
335880 2
< 0.1%
329460 1
< 0.1%
322860 1
< 0.1%
312000 1
< 0.1%
240000 1
< 0.1%
235980 1
< 0.1%
198060 1
< 0.1%

credit_type
Categorical

High correlation 

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
CIB
48152 
CRIF
43901 
EXP
41319 
EQUI
15298 

Length

Max length 4
Median length 3
Mean length 3.3981906
Min length 3

Characters and Unicode

Total characters 505209
Distinct characters 10
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row EXP
2nd row EQUI
3rd row EXP
4th row EXP
5th row CRIF

Common Values

Value Count Frequency (%)
CIB 48152
32.4%
CRIF 43901
29.5%
EXP 41319
27.8%
EQUI 15298
 
10.3%

Length

2025-11-26T22:59:03.334518 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:03.386268 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
cib 48152
32.4%
crif 43901
29.5%
exp 41319
27.8%
equi 15298
 
10.3%

Most occurring characters

Value Count Frequency (%)
I 107351
21.2%
C 92053
18.2%
E 56617
11.2%
B 48152
9.5%
R 43901
8.7%
F 43901
8.7%
X 41319
 
8.2%
P 41319
 
8.2%
Q 15298
 
3.0%
U 15298
 
3.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 505209
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
I 107351
21.2%
C 92053
18.2%
E 56617
11.2%
B 48152
9.5%
R 43901
8.7%
F 43901
8.7%
X 41319
 
8.2%
P 41319
 
8.2%
Q 15298
 
3.0%
U 15298
 
3.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 505209
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
I 107351
21.2%
C 92053
18.2%
E 56617
11.2%
B 48152
9.5%
R 43901
8.7%
F 43901
8.7%
X 41319
 
8.2%
P 41319
 
8.2%
Q 15298
 
3.0%
U 15298
 
3.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 505209
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
I 107351
21.2%
C 92053
18.2%
E 56617
11.2%
B 48152
9.5%
R 43901
8.7%
F 43901
8.7%
X 41319
 
8.2%
P 41319
 
8.2%
Q 15298
 
3.0%
U 15298
 
3.0%

Credit_Score
Real number (ℝ)

Distinct 401
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 699.7891
Minimum 500
Maximum 900
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:03.466936 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 500
5-th percentile 519
Q1 599
median 699
Q3 800
95-th percentile 881
Maximum 900
Range 400
Interquartile range (IQR) 201

Descriptive statistics

Standard deviation 115.87586
Coefficient of variation (CV) 0.16558683
Kurtosis -1.2026494
Mean 699.7891
Median Absolute Deviation (MAD) 100
Skewness 0.004766757
Sum 1.0403765 × 108
Variance 13427.214
Monotonicity Not monotonic
2025-11-26T22:59:03.574787 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
763 415
 
0.3%
867 413
 
0.3%
639 411
 
0.3%
581 408
 
0.3%
554 407
 
0.3%
737 406
 
0.3%
519 406
 
0.3%
890 406
 
0.3%
566 405
 
0.3%
687 405
 
0.3%
Other values (391) 144588
97.3%
Value Count Frequency (%)
500 357
0.2%
501 357
0.2%
502 346
0.2%
503 383
0.3%
504 392
0.3%
505 379
0.3%
506 380
0.3%
507 386
0.3%
508 400
0.3%
509 348
0.2%
Value Count Frequency (%)
900 393
0.3%
899 352
0.2%
898 370
0.2%
897 383
0.3%
896 391
0.3%
895 371
0.2%
894 361
0.2%
893 348
0.2%
892 366
0.2%
891 376
0.3%

co-applicant_credit_type
Categorical

High correlation 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
CIB
74392 
EXP
74278 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 446010
Distinct characters 6
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row CIB
2nd row EXP
3rd row CIB
4th row CIB
5th row EXP

Common Values

Value Count Frequency (%)
CIB 74392
50.0%
EXP 74278
50.0%

Length

2025-11-26T22:59:03.671405 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:03.723900 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
cib 74392
50.0%
exp 74278
50.0%

Most occurring characters

Value Count Frequency (%)
C 74392
16.7%
I 74392
16.7%
B 74392
16.7%
E 74278
16.7%
X 74278
16.7%
P 74278
16.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 446010
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
C 74392
16.7%
I 74392
16.7%
B 74392
16.7%
E 74278
16.7%
X 74278
16.7%
P 74278
16.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 446010
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
C 74392
16.7%
I 74392
16.7%
B 74392
16.7%
E 74278
16.7%
X 74278
16.7%
P 74278
16.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 446010
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
C 74392
16.7%
I 74392
16.7%
B 74392
16.7%
E 74278
16.7%
X 74278
16.7%
P 74278
16.7%

age
Categorical

Distinct 7
Distinct (%) < 0.1%
Missing 200
Missing (%) 0.1%
Memory size 1.1 MiB
45-54
34720 
35-44
32818 
55-64
32534 
65-74
20744 
25-34
19142 
Other values (2)
8512 

Length

Max length 5
Median length 5
Mean length 4.8853371
Min length 3

Characters and Unicode

Total characters 725326
Distinct characters 9
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 25-34
2nd row 55-64
3rd row 35-44
4th row 45-54
5th row 25-34

Common Values

Value Count Frequency (%)
45-54 34720
23.4%
35-44 32818
22.1%
55-64 32534
21.9%
65-74 20744
14.0%
25-34 19142
12.9%
>74 7175
 
4.8%
<25 1337
 
0.9%
(Missing) 200
 
0.1%

Length

2025-11-26T22:59:03.790285 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:03.862002 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
45-54 34720
23.4%
35-44 32818
22.1%
55-64 32534
21.9%
65-74 20744
14.0%
25-34 19142
12.9%
74 7175
 
4.8%
25 1337
 
0.9%

Most occurring characters

Value Count Frequency (%)
4 214671
29.6%
5 208549
28.8%
- 139958
19.3%
6 53278
 
7.3%
3 51960
 
7.2%
7 27919
 
3.8%
2 20479
 
2.8%
> 7175
 
1.0%
< 1337
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 725326
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
4 214671
29.6%
5 208549
28.8%
- 139958
19.3%
6 53278
 
7.3%
3 51960
 
7.2%
7 27919
 
3.8%
2 20479
 
2.8%
> 7175
 
1.0%
< 1337
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 725326
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
4 214671
29.6%
5 208549
28.8%
- 139958
19.3%
6 53278
 
7.3%
3 51960
 
7.2%
7 27919
 
3.8%
2 20479
 
2.8%
> 7175
 
1.0%
< 1337
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 725326
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
4 214671
29.6%
5 208549
28.8%
- 139958
19.3%
6 53278
 
7.3%
3 51960
 
7.2%
7 27919
 
3.8%
2 20479
 
2.8%
> 7175
 
1.0%
< 1337
 
0.2%
Distinct 2
Distinct (%) < 0.1%
Missing 200
Missing (%) 0.1%
Memory size 1.1 MiB
to_inst
95814 
not_inst
52656 

Length

Max length 8
Median length 7
Mean length 7.3546575
Min length 7

Characters and Unicode

Total characters 1091946
Distinct characters 6
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row to_inst
2nd row to_inst
3rd row to_inst
4th row not_inst
5th row not_inst

Common Values

Value Count Frequency (%)
to_inst 95814
64.4%
not_inst 52656
35.4%
(Missing) 200
 
0.1%

Length

2025-11-26T22:59:03.948815 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:03.994903 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
to_inst 95814
64.5%
not_inst 52656
35.5%

Most occurring characters

Value Count Frequency (%)
t 296940
27.2%
n 201126
18.4%
o 148470
13.6%
_ 148470
13.6%
i 148470
13.6%
s 148470
13.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 1091946
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
t 296940
27.2%
n 201126
18.4%
o 148470
13.6%
_ 148470
13.6%
i 148470
13.6%
s 148470
13.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1091946
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
t 296940
27.2%
n 201126
18.4%
o 148470
13.6%
_ 148470
13.6%
i 148470
13.6%
s 148470
13.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1091946
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
t 296940
27.2%
n 201126
18.4%
o 148470
13.6%
_ 148470
13.6%
i 148470
13.6%
s 148470
13.6%

LTV
Real number (ℝ)

Missing  Skewed 

Distinct 8484
Distinct (%) 6.4%
Missing 15098
Missing (%) 10.2%
Infinite 0
Infinite (%) 0.0%
Mean 72.746457
Minimum 0.9674782
Maximum 7831.25
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:04.069414 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 0.9674782
5-th percentile 36.350575
Q1 60.47486
median 75.13587
Q3 86.184211
95-th percentile 98.728814
Maximum 7831.25
Range 7830.2825
Interquartile range (IQR) 25.70935

Descriptive statistics

Standard deviation 39.967603
Coefficient of variation (CV) 0.54940961
Kurtosis 19979.045
Mean 72.746457
Median Absolute Deviation (MAD) 12.514733
Skewness 120.61534
Sum 9716889.8
Variance 1597.4093
Monotonicity Not monotonic
2025-11-26T22:59:04.172414 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
81.25 530
 
0.4%
91.66666667 499
 
0.3%
80.03875969 380
 
0.3%
80.03246753 328
 
0.2%
94.95614035 322
 
0.2%
78.84615385 317
 
0.2%
78.64583333 310
 
0.2%
80.06329114 309
 
0.2%
79.04040404 309
 
0.2%
95.16806723 306
 
0.2%
Other values (8474) 129962
87.4%
(Missing) 15098
 
10.2%
Value Count Frequency (%)
0.967478198 1
< 0.1%
2.072942643 1
< 0.1%
2.767587397 1
< 0.1%
2.81374502 1
< 0.1%
2.856420627 1
< 0.1%
2.992584746 1
< 0.1%
3.083554377 1
< 0.1%
3.125 1
< 0.1%
3.74668435 1
< 0.1%
3.875171468 1
< 0.1%
Value Count Frequency (%)
7831.25 1
< 0.1%
6706.25 1
< 0.1%
5206.25 1
< 0.1%
4706.25 1
< 0.1%
2956.25 1
< 0.1%
2331.25 1
< 0.1%
263.5416667 1
< 0.1%
237.5 2
< 0.1%
220.3629032 1
< 0.1%
201.7857143 1
< 0.1%

Region
Categorical

Distinct 4
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
North
74722 
south
64016 
central
8697 
North-East
 
1235

Length

Max length 10
Median length 5
Mean length 5.1585323
Min length 5

Characters and Unicode

Total characters 766919
Distinct characters 14
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row south
2nd row North
3rd row south
4th row North
5th row North

Common Values

Value Count Frequency (%)
North 74722
50.3%
south 64016
43.1%
central 8697
 
5.8%
North-East 1235
 
0.8%

Length

2025-11-26T22:59:04.268747 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:04.324176 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
north 74722
50.3%
south 64016
43.1%
central 8697
 
5.8%
north-east 1235
 
0.8%

Most occurring characters

Value Count Frequency (%)
t 149905
19.5%
o 139973
18.3%
h 139973
18.3%
r 84654
11.0%
N 75957
9.9%
s 65251
8.5%
u 64016
8.3%
a 9932
 
1.3%
e 8697
 
1.1%
c 8697
 
1.1%
Other values (4) 19864
 
2.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 766919
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
t 149905
19.5%
o 139973
18.3%
h 139973
18.3%
r 84654
11.0%
N 75957
9.9%
s 65251
8.5%
u 64016
8.3%
a 9932
 
1.3%
e 8697
 
1.1%
c 8697
 
1.1%
Other values (4) 19864
 
2.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 766919
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
t 149905
19.5%
o 139973
18.3%
h 139973
18.3%
r 84654
11.0%
N 75957
9.9%
s 65251
8.5%
u 64016
8.3%
a 9932
 
1.3%
e 8697
 
1.1%
c 8697
 
1.1%
Other values (4) 19864
 
2.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 766919
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
t 149905
19.5%
o 139973
18.3%
h 139973
18.3%
r 84654
11.0%
N 75957
9.9%
s 65251
8.5%
u 64016
8.3%
a 9932
 
1.3%
e 8697
 
1.1%
c 8697
 
1.1%
Other values (4) 19864
 
2.6%

Security_Type
Categorical

High correlation  Imbalance 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
direct
148637 
Indriect
 
33

Length

Max length 8
Median length 6
Mean length 6.0004439
Min length 6

Characters and Unicode

Total characters 892086
Distinct characters 8
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row direct
2nd row direct
3rd row direct
4th row direct
5th row direct

Common Values

Value Count Frequency (%)
direct 148637
> 99.9%
Indriect 33
 
< 0.1%

Length

2025-11-26T22:59:04.400764 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:04.453542 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
direct 148637
> 99.9%
indriect 33
 
< 0.1%

Most occurring characters

Value Count Frequency (%)
d 148670
16.7%
i 148670
16.7%
r 148670
16.7%
e 148670
16.7%
c 148670
16.7%
t 148670
16.7%
I 33
 
< 0.1%
n 33
 
< 0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 892086
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
d 148670
16.7%
i 148670
16.7%
r 148670
16.7%
e 148670
16.7%
c 148670
16.7%
t 148670
16.7%
I 33
 
< 0.1%
n 33
 
< 0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 892086
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
d 148670
16.7%
i 148670
16.7%
r 148670
16.7%
e 148670
16.7%
c 148670
16.7%
t 148670
16.7%
I 33
 
< 0.1%
n 33
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 892086
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
d 148670
16.7%
i 148670
16.7%
r 148670
16.7%
e 148670
16.7%
c 148670
16.7%
t 148670
16.7%
I 33
 
< 0.1%
n 33
 
< 0.1%

Status
Categorical

High correlation 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.1 MiB
0
112031 
1
36639 

Length

Max length 1
Median length 1
Mean length 1
Min length 1

Characters and Unicode

Total characters 148670
Distinct characters 2
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1
2nd row 1
3rd row 0
4th row 0
5th row 0

Common Values

Value Count Frequency (%)
0 112031
75.4%
1 36639
 
24.6%

Length

2025-11-26T22:59:04.522698 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T22:59:04.576416 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Value Count Frequency (%)
0 112031
75.4%
1 36639
 
24.6%

Most occurring characters

Value Count Frequency (%)
0 112031
75.4%
1 36639
 
24.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 148670
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 112031
75.4%
1 36639
 
24.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 148670
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 112031
75.4%
1 36639
 
24.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 148670
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 112031
75.4%
1 36639
 
24.6%

dtir1
Real number (ℝ)

Missing 

Distinct 57
Distinct (%) < 0.1%
Missing 24121
Missing (%) 16.2%
Infinite 0
Infinite (%) 0.0%
Mean 37.732932
Minimum 5
Maximum 61
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.1 MiB
2025-11-26T22:59:04.639839 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum 5
5-th percentile 20
Q1 31
median 39
Q3 45
95-th percentile 54
Maximum 61
Range 56
Interquartile range (IQR) 14

Descriptive statistics

Standard deviation 10.545435
Coefficient of variation (CV) 0.27947563
Kurtosis 0.37888256
Mean 37.732932
Median Absolute Deviation (MAD) 7
Skewness -0.55146496
Sum 4699599
Variance 111.2062
Monotonicity Not monotonic
2025-11-26T22:59:04.733199 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
37 6848
 
4.6%
36 6553
 
4.4%
44 6500
 
4.4%
49 6309
 
4.2%
43 5307
 
3.6%
42 5121
 
3.4%
41 4881
 
3.3%
40 4699
 
3.2%
39 4540
 
3.1%
38 4461
 
3.0%
Other values (47) 69330
46.6%
(Missing) 24121
 
16.2%
Value Count Frequency (%)
5 386
0.3%
6 420
0.3%
7 379
0.3%
8 433
0.3%
9 395
0.3%
10 386
0.3%
11 400
0.3%
12 383
0.3%
13 421
0.3%
14 393
0.3%
Value Count Frequency (%)
61 692
0.5%
60 832
0.6%
59 812
0.5%
58 757
0.5%
57 823
0.6%
56 746
0.5%
55 798
0.5%
54 832
0.6%
53 787
0.5%
52 777
0.5%

Interactions

2025-11-26T22:58:56.796098 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:45.444764 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.638138 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.787262 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.888000 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.945574 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.020451 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.092077 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:53.196513 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.431821 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.619709 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.896853 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:45.559922 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.747314 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.892545 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.987331 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.049428 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.122171 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.206977 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:53.307353 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.551754 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.731097 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.991184 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:45.662908 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.846303 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.995044 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.089152 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.146198 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.217684 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.301354 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:53.403387 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.654837 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.830098 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:57.079452 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:45.776413 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.948122 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.102367 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.194504 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.243987 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.307199 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.392262 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:53.508507 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.760657 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.932122 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:57.166621 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:45.877498 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.045980 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.200400 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.287769 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.340267 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.395021 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.479810 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:53.605353 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.867598 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.068986 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:57.265501 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:45.990430 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.151284 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.299213 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.378707 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.432152 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.501771 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.583880 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:53.712138 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.979983 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.178342 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:57.356983 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.096507 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.252169 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.392273 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.470473 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.523000 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.593554 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.687010 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:53.803161 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.087162 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.287672 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:57.460075 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.206672 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.357724 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.484013 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.556277 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.622699 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.693780 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.784554 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.039773 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.195195 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.392061 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:57.555917 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.312761 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.464759 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.584139 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.655748 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.722199 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.792752 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.886597 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.142214 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.304909 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.492894 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:57.652816 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.427166 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.580773 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.692002 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.760614 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.817717 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.891639 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:52.991127 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.244963 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.416080 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.596970 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:57.746635 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:46.533968 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:47.690557 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:48.792833 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:49.858019 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:50.916971 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:51.992492 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:53.091866 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:54.335383 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:55.519476 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
2025-11-26T22:58:56.704731 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-26T22:59:04.841467 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Credit_Score Credit_Worthiness Gender ID Interest_rate_spread LTV Neg_ammortization Region Secured_by Security_Type Status Upfront_charges age approv_in_adv business_or_commercial co-applicant_credit_type construction_type credit_type dtir1 income interest_only loan_amount loan_limit loan_purpose loan_type lump_sum_payment occupancy_type open_credit property_value rate_of_interest submission_of_application term total_units
Credit_Score 1.000 0.006 0.000 -0.001 -0.003 -0.004 0.002 0.000 0.007 0.007 0.005 -0.002 0.004 0.000 0.000 0.000 0.007 0.003 -0.000 0.001 0.000 0.005 0.000 0.007 0.000 0.000 0.003 0.000 0.005 -0.002 0.006 -0.005 0.003
Credit_Worthiness 0.006 1.000 0.004 0.006 0.056 0.012 0.059 0.005 0.000 0.000 0.035 0.000 0.010 0.062 0.001 0.013 0.000 0.023 0.015 0.013 0.050 0.014 0.024 0.050 0.013 0.031 0.007 0.230 0.038 0.165 0.022 0.220 0.008
Gender 0.000 0.004 1.000 0.000 0.072 0.002 0.017 0.376 0.003 0.003 0.084 0.046 0.071 0.012 0.048 0.665 0.003 0.023 0.038 0.009 0.012 0.113 0.039 0.069 0.080 0.012 0.020 0.022 0.023 0.039 0.360 0.045 0.013
ID -0.001 0.006 0.000 1.000 0.003 -0.005 0.000 0.000 0.005 0.005 0.004 -0.005 0.000 0.000 0.000 0.007 0.005 0.005 -0.007 0.004 0.006 -0.001 0.000 0.000 0.002 0.000 0.000 0.008 0.001 -0.000 0.002 -0.002 0.004
Interest_rate_spread -0.003 0.056 0.072 0.003 1.000 0.111 0.037 0.022 1.000 1.000 1.000 0.100 0.034 0.056 0.385 0.068 1.000 0.026 0.075 -0.251 0.013 -0.426 0.055 0.189 0.412 0.010 0.128 0.058 -0.452 0.583 0.299 -0.166 0.027
LTV -0.004 0.012 0.002 -0.005 0.111 1.000 0.000 0.000 0.000 0.000 0.003 -0.115 0.000 0.003 0.016 0.004 0.000 0.000 0.156 -0.023 0.000 0.083 0.008 0.000 0.010 0.000 0.000 0.000 -0.365 0.030 0.006 0.210 0.000
Neg_ammortization 0.002 0.059 0.017 0.000 0.037 0.000 1.000 0.007 0.006 0.006 0.156 0.025 0.015 0.078 0.014 0.010 0.006 0.079 0.031 0.008 0.018 0.029 0.008 0.073 0.019 0.051 0.031 0.000 0.010 0.177 0.051 0.199 0.014
Region 0.000 0.005 0.376 0.000 0.022 0.000 0.007 1.000 0.004 0.004 0.050 0.030 0.024 0.008 0.054 0.030 0.004 0.015 0.026 0.000 0.000 0.005 0.007 0.047 0.051 0.013 0.032 0.009 0.008 0.012 0.145 0.028 0.006
Secured_by 0.007 0.000 0.003 0.005 1.000 0.000 0.006 0.004 1.000 0.985 0.025 1.000 0.004 0.000 0.006 0.005 0.985 0.003 0.000 0.000 0.000 0.000 0.002 0.004 0.007 0.005 0.000 0.000 0.000 1.000 0.000 0.000 0.000
Security_Type 0.007 0.000 0.003 0.005 1.000 0.000 0.006 0.004 0.985 1.000 0.025 1.000 0.004 0.000 0.006 0.005 0.985 0.003 0.000 0.000 0.000 0.000 0.002 0.004 0.007 0.005 0.000 0.000 0.000 1.000 0.000 0.000 0.000
Status 0.005 0.035 0.084 0.004 1.000 0.003 0.156 0.050 0.025 0.025 1.000 0.010 0.050 0.037 0.092 0.144 0.025 0.592 0.224 0.009 0.014 0.085 0.054 0.040 0.094 0.188 0.030 0.010 0.025 0.025 0.121 0.102 0.029
Upfront_charges -0.002 0.000 0.046 -0.005 0.100 -0.115 0.025 0.030 1.000 1.000 0.010 1.000 0.029 0.005 0.107 0.034 1.000 0.009 -0.017 -0.080 0.000 -0.116 0.075 0.087 0.084 0.000 0.026 1.000 -0.075 -0.052 0.165 -0.116 0.037
age 0.004 0.010 0.071 0.000 0.034 0.000 0.015 0.024 0.004 0.004 0.050 0.029 1.000 0.032 0.079 0.049 0.004 0.020 0.033 0.007 0.007 0.081 0.032 0.191 0.108 0.011 0.060 0.040 0.024 0.030 0.270 0.056 0.007
approv_in_adv 0.000 0.062 0.012 0.000 0.056 0.003 0.078 0.008 0.000 0.000 0.037 0.005 0.032 1.000 0.010 0.012 0.000 0.018 0.017 0.000 0.074 0.029 0.096 0.154 0.013 0.061 0.020 0.005 0.016 0.083 0.081 0.047 0.004
business_or_commercial 0.000 0.001 0.048 0.000 0.385 0.016 0.014 0.054 0.006 0.006 0.092 0.107 0.079 0.010 1.000 0.023 0.006 0.028 0.303 0.012 0.007 0.142 0.022 0.065 1.000 0.014 0.111 0.024 0.044 0.050 0.090 0.102 0.026
co-applicant_credit_type 0.000 0.013 0.665 0.007 0.068 0.004 0.010 0.030 0.005 0.005 0.144 0.034 0.049 0.012 0.023 1.000 0.005 0.339 0.053 0.008 0.014 0.130 0.041 0.045 0.050 0.034 0.026 0.016 0.033 0.044 0.062 0.018 0.000
construction_type 0.007 0.000 0.003 0.005 1.000 0.000 0.006 0.004 0.985 0.985 0.025 1.000 0.004 0.000 0.006 0.005 1.000 0.003 0.000 0.000 0.000 0.000 0.002 0.004 0.007 0.005 0.000 0.000 0.000 1.000 0.000 0.000 0.000
credit_type 0.003 0.023 0.023 0.005 0.026 0.000 0.079 0.015 0.003 0.003 0.592 0.009 0.020 0.018 0.028 0.339 0.003 1.000 0.004 0.000 0.014 0.019 0.032 0.039 0.049 0.121 0.008 0.009 0.006 0.027 0.046 0.034 0.000
dtir1 -0.000 0.015 0.038 -0.007 0.075 0.156 0.031 0.026 0.000 0.000 0.224 -0.017 0.033 0.017 0.303 0.053 0.000 0.004 1.000 -0.307 0.004 0.021 0.034 0.082 0.262 0.041 0.039 0.013 -0.048 0.062 0.056 0.108 0.014
income 0.001 0.013 0.009 0.004 -0.251 -0.023 0.008 0.000 0.000 0.000 0.009 -0.080 0.007 0.000 0.012 0.008 0.000 0.000 -0.307 1.000 0.000 0.642 0.047 0.014 0.010 0.000 0.044 0.010 0.606 -0.090 0.020 -0.041 0.017
interest_only 0.000 0.050 0.012 0.006 0.013 0.000 0.018 0.000 0.000 0.000 0.014 0.000 0.007 0.074 0.007 0.014 0.000 0.014 0.004 0.000 1.000 0.000 0.031 0.022 0.011 0.033 0.011 0.273 0.040 0.134 0.010 0.024 0.003
loan_amount 0.005 0.014 0.113 -0.001 -0.426 0.083 0.029 0.005 0.000 0.000 0.085 -0.116 0.081 0.029 0.142 0.130 0.000 0.019 0.021 0.642 0.000 1.000 0.456 0.102 0.105 0.007 0.033 0.030 0.857 -0.172 0.411 0.196 0.079
loan_limit 0.000 0.024 0.039 0.000 0.055 0.008 0.008 0.007 0.002 0.002 0.054 0.075 0.032 0.096 0.022 0.041 0.002 0.032 0.034 0.047 0.031 0.456 1.000 0.041 0.063 0.019 0.015 0.018 0.150 0.053 0.011 0.075 0.008
loan_purpose 0.007 0.050 0.069 0.000 0.189 0.000 0.073 0.047 0.004 0.004 0.040 0.087 0.191 0.154 0.065 0.045 0.004 0.039 0.082 0.014 0.022 0.102 0.041 1.000 0.066 0.016 0.132 0.083 0.029 0.192 0.265 0.107 0.017
loan_type 0.000 0.013 0.080 0.002 0.412 0.010 0.019 0.051 0.007 0.007 0.094 0.084 0.108 0.013 1.000 0.050 0.007 0.049 0.262 0.010 0.011 0.105 0.063 0.066 1.000 0.014 0.109 0.034 0.039 0.220 0.110 0.105 0.028
lump_sum_payment 0.000 0.031 0.012 0.000 0.010 0.000 0.051 0.013 0.005 0.005 0.188 0.000 0.011 0.061 0.014 0.034 0.005 0.121 0.041 0.000 0.033 0.007 0.019 0.016 0.014 1.000 0.000 0.004 0.000 0.012 0.013 0.013 0.000
occupancy_type 0.003 0.007 0.020 0.000 0.128 0.000 0.031 0.032 0.000 0.000 0.030 0.026 0.060 0.020 0.111 0.026 0.000 0.008 0.039 0.044 0.011 0.033 0.015 0.132 0.109 0.000 1.000 0.014 0.000 0.191 0.067 0.024 0.169
open_credit 0.000 0.230 0.022 0.008 0.058 0.000 0.000 0.009 0.000 0.000 0.010 1.000 0.040 0.005 0.024 0.016 0.000 0.009 0.013 0.010 0.273 0.030 0.018 0.083 0.034 0.004 0.014 1.000 0.137 0.459 0.045 0.025 0.005
property_value 0.005 0.038 0.023 0.001 -0.452 -0.365 0.010 0.008 0.000 0.000 0.025 -0.075 0.024 0.016 0.044 0.033 0.000 0.006 -0.048 0.606 0.040 0.857 0.150 0.029 0.039 0.000 0.000 0.137 1.000 -0.179 0.039 0.088 0.034
rate_of_interest -0.002 0.165 0.039 -0.000 0.583 0.030 0.177 0.012 1.000 1.000 0.025 -0.052 0.030 0.083 0.050 0.044 1.000 0.027 0.062 -0.090 0.134 -0.172 0.053 0.192 0.220 0.012 0.191 0.459 -0.179 1.000 0.124 0.191 0.058
submission_of_application 0.006 0.022 0.360 0.002 0.299 0.006 0.051 0.145 0.000 0.000 0.121 0.165 0.270 0.081 0.090 0.062 0.000 0.046 0.056 0.020 0.010 0.411 0.011 0.265 0.110 0.013 0.067 0.045 0.039 0.124 1.000 0.173 0.070
term -0.005 0.220 0.045 -0.002 -0.166 0.210 0.199 0.028 0.000 0.000 0.102 -0.116 0.056 0.047 0.102 0.018 0.000 0.034 0.108 -0.041 0.024 0.196 0.075 0.107 0.105 0.013 0.024 0.025 0.088 0.191 0.173 1.000 0.013
total_units 0.003 0.008 0.013 0.004 0.027 0.000 0.014 0.006 0.000 0.000 0.029 0.037 0.007 0.004 0.026 0.000 0.000 0.000 0.014 0.017 0.003 0.079 0.008 0.017 0.028 0.000 0.169 0.005 0.034 0.058 0.070 0.013 1.000

Missing values

2025-11-26T22:58:58.012139 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-26T22:58:58.516335 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-26T22:58:59.275878 image/svg+xml Matplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID year loan_limit Gender approv_in_adv loan_type loan_purpose Credit_Worthiness open_credit business_or_commercial loan_amount rate_of_interest Interest_rate_spread Upfront_charges term Neg_ammortization interest_only lump_sum_payment property_value construction_type occupancy_type Secured_by total_units income credit_type Credit_Score co-applicant_credit_type age submission_of_application LTV Region Security_Type Status dtir1
0 24890 2019 cf Sex Not Available nopre type1 p1 l1 nopc nob/c 116500 NaN NaN NaN 360.0 not_neg not_int not_lpsm 118000.0 sb pr home 1U 1740.0 EXP 758 CIB 25-34 to_inst 98.728814 south direct 1 45.0
1 24891 2019 cf Male nopre type2 p1 l1 nopc b/c 206500 NaN NaN NaN 360.0 not_neg not_int lpsm NaN sb pr home 1U 4980.0 EQUI 552 EXP 55-64 to_inst NaN North direct 1 NaN
2 24892 2019 cf Male pre type1 p1 l1 nopc nob/c 406500 4.560 0.2000 595.00 360.0 neg_amm not_int not_lpsm 508000.0 sb pr home 1U 9480.0 EXP 834 CIB 35-44 to_inst 80.019685 south direct 0 46.0
3 24893 2019 cf Male nopre type1 p4 l1 nopc nob/c 456500 4.250 0.6810 NaN 360.0 not_neg not_int not_lpsm 658000.0 sb pr home 1U 11880.0 EXP 587 CIB 45-54 not_inst 69.376900 North direct 0 42.0
4 24894 2019 cf Joint pre type1 p1 l1 nopc nob/c 696500 4.000 0.3042 0.00 360.0 not_neg not_int not_lpsm 758000.0 sb pr home 1U 10440.0 CRIF 602 EXP 25-34 not_inst 91.886544 North direct 0 39.0
5 24895 2019 cf Joint pre type1 p1 l1 nopc nob/c 706500 3.990 0.1523 370.00 360.0 not_neg not_int not_lpsm 1008000.0 sb pr home 1U 10080.0 EXP 864 EXP 35-44 not_inst 70.089286 North direct 0 40.0
6 24896 2019 cf Joint pre type1 p3 l1 nopc nob/c 346500 4.500 0.9998 5120.00 360.0 not_neg not_int not_lpsm 438000.0 sb pr home 1U 5040.0 EXP 860 EXP 55-64 to_inst 79.109589 North direct 0 44.0
7 24897 2019 NaN Female nopre type1 p4 l1 nopc nob/c 266500 4.125 0.2975 5609.88 360.0 not_neg not_int not_lpsm 308000.0 sb pr home 1U 3780.0 CIB 863 CIB 55-64 to_inst 86.525974 North direct 0 42.0
8 24898 2019 cf Joint nopre type1 p3 l1 nopc nob/c 376500 4.875 0.7395 1150.00 360.0 not_neg not_int not_lpsm 478000.0 sb pr home 1U 5580.0 CIB 580 EXP 55-64 to_inst 78.765690 central direct 0 44.0
9 24899 2019 cf Sex Not Available nopre type3 p3 l1 nopc nob/c 436500 3.490 -0.2776 2316.50 360.0 not_neg not_int not_lpsm 688000.0 sb pr home 1U 6720.0 CIB 788 EXP 55-64 to_inst 63.444767 south direct 0 30.0
ID year loan_limit Gender approv_in_adv loan_type loan_purpose Credit_Worthiness open_credit business_or_commercial loan_amount rate_of_interest Interest_rate_spread Upfront_charges term Neg_ammortization interest_only lump_sum_payment property_value construction_type occupancy_type Secured_by total_units income credit_type Credit_Score co-applicant_credit_type age submission_of_application LTV Region Security_Type Status dtir1
148660 173550 2019 cf Female nopre type1 p4 l1 nopc nob/c 366500 3.875 -0.1171 3643.16 360.0 not_neg not_int not_lpsm 658000.0 sb pr home 1U 7200.0 CIB 851 EXP 45-54 not_inst 55.699088 North direct 0 20.0
148661 173551 2019 cf Sex Not Available nopre type2 p4 l1 nopc b/c 346500 NaN NaN NaN 360.0 not_neg not_int not_lpsm 358000.0 sb pr home 1U NaN EXP 585 CIB 25-34 to_inst 96.787710 south direct 1 NaN
148662 173552 2019 cf Joint nopre type1 p4 l1 nopc nob/c 646500 3.625 0.0743 7639.80 360.0 not_neg int_only not_lpsm 828000.0 sb pr home 1U 13500.0 CIB 873 EXP 45-54 not_inst 78.079710 North direct 0 31.0
148663 173553 2019 cf Male nopre type2 p1 l1 nopc b/c 106500 NaN NaN NaN 360.0 not_neg not_int not_lpsm NaN sb pr home 1U 1860.0 EQUI 619 EXP <25 to_inst NaN North direct 1 NaN
148664 173554 2019 cf Joint nopre type2 p1 l1 nopc b/c 156500 3.990 1.4015 3113.06 360.0 not_neg not_int not_lpsm 158000.0 sb pr home 1U 4020.0 EXP 859 EXP 65-74 to_inst 99.050633 central direct 0 45.0
148665 173555 2019 cf Sex Not Available nopre type1 p3 l1 nopc nob/c 436500 3.125 0.2571 9960.00 180.0 not_neg not_int not_lpsm 608000.0 sb pr home 1U 7860.0 CIB 659 EXP 55-64 to_inst 71.792763 south direct 0 48.0
148666 173556 2019 cf Male nopre type1 p1 l1 nopc nob/c 586500 5.190 0.8544 0.00 360.0 not_neg not_int not_lpsm 788000.0 sb ir home 4U 7140.0 CIB 569 CIB 25-34 not_inst 74.428934 south direct 0 15.0
148667 173557 2019 cf Male nopre type1 p4 l1 nopc nob/c 446500 3.125 0.0816 1226.64 180.0 not_neg not_int not_lpsm 728000.0 sb pr home 1U 6900.0 CIB 702 EXP 45-54 not_inst 61.332418 North direct 0 49.0
148668 173558 2019 cf Female nopre type1 p4 l1 nopc nob/c 196500 3.500 0.5824 4323.33 180.0 not_neg not_int not_lpsm 278000.0 sb pr home 1U 7140.0 EXP 737 EXP 55-64 to_inst 70.683453 North direct 0 29.0
148669 173559 2019 cf Female nopre type1 p3 l1 nopc nob/c 406500 4.375 1.3871 6000.00 240.0 not_neg not_int not_lpsm 558000.0 sb pr home 1U 7260.0 CIB 830 CIB 45-54 not_inst 72.849462 North direct 0 44.0